modeling_roberta_sentiment.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerBlock(nn.Module):
    def __init__(self, hidden_dim, num_heads, ffn_dim, dropout):
        super().__init__()
        self.attn_norm = nn.LayerNorm(hidden_dim)
        self.ffn_norm = nn.LayerNorm(hidden_dim)
        self.attn = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout, batch_first=True)
        self.ffn = nn.Sequential(
            nn.Linear(hidden_dim, ffn_dim),
            nn.GELU(),
            nn.Linear(ffn_dim, hidden_dim),
            nn.Dropout(dropout)
        )
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, attention_mask):
        batch_size, seq_len, _ = x.size()
    
        # No transpose needed since batch_first=True
        x_norm = self.attn_norm(x)
        attn_mask = (1 - attention_mask).bool()  # [batch_size, seq_len]
    
        assert attn_mask.shape == (batch_size, seq_len), \
            f"Expected {batch_size=} and {seq_len=}, got {attn_mask.shape}"
    
        # Run self-attention (no transpose)
        attn_out, _ = self.attn(
            x_norm, x_norm, x_norm,
            key_padding_mask=attn_mask
        )
    
        # Residual + FF
        x = x + self.dropout(attn_out)
        x_norm = self.ffn_norm(x)
        x = x + self.dropout(self.ffn(x_norm))
    
        return x

class RobertaForSentimentClassification(nn.Module):
    def __init__(self, vocab_size, max_len=128, num_classes=5):
        super().__init__()
        self.hidden_size = 512
        self.max_len = max_len
        self.num_heads = 8
        self.ffn_dim = 2048
        self.num_layers = 6
        self.dropout_rate = 0.1

        # Embeddings
        self.token_emb = nn.Embedding(vocab_size, self.hidden_size)
        self.position_emb = nn.Embedding(max_len, self.hidden_size)
        self.dropout = nn.Dropout(self.dropout_rate)

        # Transformer blocks
        self.layers = nn.ModuleList([
            TransformerBlock(self.hidden_size, self.num_heads, self.ffn_dim, self.dropout_rate)
            for _ in range(self.num_layers)
        ])

        # Classification head
        self.classifier = nn.Sequential(
            nn.Linear(self.hidden_size, self.hidden_size),
            nn.GELU(),
            nn.Dropout(self.dropout_rate),
            nn.Linear(self.hidden_size, num_classes)
        )

    def forward(self, input_ids, attention_mask):
        batch_size, seq_len = input_ids.size()

        # Embeddings
        positions = torch.arange(0, seq_len, device=input_ids.device).unsqueeze(0).expand(batch_size, seq_len)
        x = self.token_emb(input_ids) + self.position_emb(positions)
        x = self.dropout(x)

        # Transformer blocks
        for layer in self.layers:
            x = layer(x, attention_mask)

        # Use <s> token (first position) for classification
        cls_token = x[:, 0]  # shape: (batch_size, hidden_size)
        logits = self.classifier(cls_token)
        return logits